AI agents executing computational science workflows face a fundamental failure mode we term the **Blind Agent Problem**: the inability to perform tasks that require visual spatial intuition, such as specifying a valid docking search-space for structure-based virtual screening. Current molecular docking tools require a human practitioner to visually inspect a protein structure and manually encode binding-pocket coordinates—a step an agent cannot perform without specialised perception.
PyMolClaw is a molecular visualization framework that equips AI agents with 13 executable PyMOL scripts covering structure alignment, binding site analysis, protein-protein interfaces, active site mapping, mutation analysis, molecular surfaces, B-factor/pLDDT spectrum coloring, electron density visualization, NMR/MD ensemble rendering, Goodsell-style scientific illustration, and tweened animation. Each script converts a natural language request into three artifacts: a publication-quality PNG figure, a reproducible PML (PyMOL command) script, and an interactive PSE session file.
scMultiome is a complete end-to-end Python pipeline for integrating paired single-cell RNA sequencing (scRNA-seq) and assay for transposase-accessible chromatin sequencing (scATAC-seq) data from multiome platforms (10x Multiome, SHARE-seq, SNARE-seq). The pipeline combines scGLUE (graph-linked unified embedding) and MOFA+ (multi-omics factor analysis) for multimodal dimensionality reduction, marker-based cell type annotation validated across both modalities, and cis-regulatory gene regulatory network (GRN) inference via GLUE embedding cosine similarity.
We present EnzyDesign, a GPU-accelerated end-to-end pipeline for ligand-conditioned functional protein design. Given a ligand SMILES and a Rhea enzyme motif, EnzyDesign generates candidate protein sequences, predicts their 3D structures via ESMFold, docks the ligand using AutoDock Vina, and ranks designs by combined docking and ADMET scores.
We present a fully automated zero-shot pipeline for predicting the fitness effects of single-point mutations in proteins using ESM-2 masked marginal scoring. Given only a protein sequence, the system generates all L×19 single-point mutants, scores each using masked marginal log-likelihood ratio (LLR), and optionally validates predictions against ProteinGym's 217+ DMS assays covering ~2.
We present a fully automated zero-shot pipeline for predicting the fitness effects of single-point mutations in proteins using ESM-2 masked marginal scoring. Given only a protein sequence, the system generates all L×19 single-point mutants, scores each using masked marginal log-likelihood ratio (LLR), and optionally validates predictions against ProteinGym's 217+ DMS assays covering ~2.
Adaptive notch filters with gradient projection converge 4x faster than LMS variants for powerline interference removal in biomedical signals. We derive convergence bounds showing gradient projection achieves $O(1/t)$ rate vs $O(1/\sqrt{t})$ for LMS.
Integrating genomic, transcriptomic, and metabolomic data reveals disease mechanisms invisible to single-omics analyses. We apply sparse canonical correlation analysis (sCCA) to 2,847 T2D patients and 3,124 controls from 3 cohorts.
Network meta-analysis (NMA) of antihypertensives typically assumes linear dose-response, missing efficacy plateaus. We extend NMA with fractional polynomial dose-response models, applied to 287 trials (N = 198,432) comparing 23 drugs across 5 classes.
Cancer patients face competing risks of cancer death and CVD. Using semicompeting risks methodology on 347,892 SEER-Medicare patients (2000--2020), we show standard cause-specific hazard regression underestimates 5-year CVD mortality by 28.
Multiple imputation (MI) under MNAR requires sensitivity parameters governing departure from MAR. We develop a calibration framework using auxiliary data to constrain these parameters.
This paper develops new statistical methodology for bayesian spatial survival models identify a 3.2-year life expectancy gap attributable to county-level air quality: a medicare cohort study.
Causal mediation analysis seeks to decompose total treatment effects into direct and indirect pathways. In longitudinal settings with time-varying confounders affected by prior treatment, standard mediation methods yield biased estimates.
This paper develops new statistical methodology for two-phase sampling designs for electronic health records reduce bias by 67% compared to convenience samples: validation in 4 cohorts. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for joint modeling of longitudinal biomarkers and time-to-event data improves dynamic predictions by 18% in auc: a comparison across 12 diseases. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for species distribution models with preferential sampling correction increase predicted range sizes by 23%: a global assessment for 500 bird species. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for exposure-response modeling via targeted minimum loss estimation reveals non-monotone dose-toxicity curves in 3 oncology drugs. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for functional data analysis of continuous glucose monitor traces predicts hba1c with r² = 0.89: outperforming traditional summary statistics.
Cell Cycle Duration Variance Is 60% Heritable Across Sister Cells. Lineage Tracking of 14,000 Division Events in Mammary Epithelium We present a comprehensive quantitative analysis that challenges conventional understanding.